Currently, collaborative filters exist to serve a marketplace where an item is relatively low-priced (e.g., packaged groceries), consumed frequently (e.g., movie rentals), bundled with other items (e.g., a consumer who buys a razor may also buy replacement blades), and/or there is little measurable similarity between items (e.g., books). When a consumer does not have information relevant to a specifically desired product or does not understand such information, the consumer can be at a serious negotiation disadvantage. Exacerbating this problem is the fact that complex, negotiated transactions can be difficult for consumers to understand due to a variety of factors, including interdependence between local demand and availability of products or product features, the point-in-time in the product lifecycle at which a transaction occurs, and the interrelationships of various transactions to one another. For example, a seller may sacrifice margin on one aspect of one transaction and recoup that margin from another transaction with the same (or a different) customer.
For items involving complex transactions, currently available data is generally single dimensional. To illustrate with a specific example, a recommended price (e.g., $20,000) for item A may not take into account how sensitive that price is (“Is $19,000 a good or bad price for this item?”) or how item A compares to item B at about the same price. Consequently, there is always room for improvement.